Human Motion Generation: A Survey

Human motion generation aims to generate natural human pose sequences and shows immense potential for real-world applications. Substantial progress has been made recently in motion data collection technologies and generation methods, laying the foundation for increasing interest in human motion generation. Most research within this field focuses on generating human motions based on conditional signals, such as text, audio, and scene contexts. While significant advancements have been made in recent years, the task continues to pose challenges due to the intricate nature of human motion and its implicit relationship with conditional signals. In this survey, we present a comprehensive literature review of human motion generation, which, to the best of our knowledge, is the first of its kind in this field. We begin by introducing the background of human motion and generative models, followed by an examination of representative methods for three mainstream sub-tasks: text-conditioned, audio-conditioned, and scene-conditioned human motion generation. Additionally, we provide an overview of common datasets and evaluation metrics. Lastly, we discuss open problems and outline potential future research directions. We hope that this survey could provide the community with a comprehensive glimpse of this rapidly evolving field and inspire novel ideas that address the outstanding challenges.Comment: 20 pages, 5 figure

Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA) based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA) based on kernel k-means (KKM) and ratio cut (RC) objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-ofthe-art approaches

Downregulation of TLX induces TET3 expression and inhibits glioblastoma stem cell self-renewal and tumorigenesis

International audienceGlioblastomas have been proposed to be maintained by highly tumorigenic glioblastoma stem cells (GSCs) that are resistant to current therapy. Therefore, targeting GSCs is critical for developing effective therapies for glioblastoma. In this study, we identify the regulatory cascade of the nuclear receptor TLX and the DNA hydroxylase Ten eleven translocation 3 (TET3) as a target for human GSCs. We show that knockdown of TLX expression inhibits human GSC tumorigenicity in mice. Treatment of human GSC-grafted mice with viral vector-delivered TLX shRNA or nanovector-delivered TLX siRNA inhibits tumour development and prolongs survival. Moreover, we identify TET3 as a potent tumour suppressor downstream of TLX to regulate the growth and self-renewal in GSCs. This study identifies the TLX-TET3 axis as a potential therapeutic target for glioblastoma

Structural and functional studies of novel mechanisms of Lassa fever virus nucleoprotein in immune suppression, viral RNA transcription and replication

Lassa fever virus is one of the most dangerous viruses of arenaviridae family, causing more than 500,000 infections per year in Africa. The fatality rate for hospitalized patients is as high as 20%. Due to the high fatality and lack of efficient licensed drugs and vaccines to treat and prevent, Lassa fever virus is classified as a Category A priority pathogen and biosafety level-4 agent by the Centers for Disease Control and Prevention of the USA. Cases were also found in the Americas and European countries, highlighting its potency to be a bioterrorism weapon. Like other areanaviruses, Lassa virus has developed a unique interferon suppression mechanism to evade from the host immune system, in which Lassa nucleoprotein plays the key role. To understand the LASV nucleoprotein functions, we tried to determine the first arenaviral nucleoprotein structure, LASV nucleoprotein. The LASV nucleoprotein (NP) was overexpressed and purified. The NP protein was crystallized and the structure was determined to 1.80 Å resolution. The crystals belong to space group P3, with the unit cell parameters a = b = 177.16 Å, c = 56.49 Å, α= β= 90° and γ= 120°. The LASV NP structure contains two domains, which are not similar to any reported viral nucleoprotein structures. The N-terminal domain has a novel structure with a cavity, which we proposed for cap binding, and the C-terminus is a 3’-5' ribonuclease, which is responsible for suppressing interferon production. To characterize the possible interaction between NP and other arenaviral protein, we also overexpressed and purified LASV Z. Interestingly, both NP and Z proteins have two forms and the purified NP protein and monomeric Z protein bind RNA. It is surprising that only the oligomeric Z protein interacts with NP protein but the monomeric Z protein does not as determined by Isothermal Titration Calorimetry (ITC). Our studies have reported the first arenaviral nucleoprotein structure, revealed the novel mechanism for the cap binding and immune suppression, which set up a platform for the development of novel drugs and vaccines to treat deadly arenaviral infections

Experimental study of castor oil–diesel–n-butanol blends used in a CRDI diesel engine with double injection strategy

In the present study, experimental investigations were carried out on a diesel engine fuelled with castor oil–diesel–n-butanol blends to determine the combustion characteristics and emissions. In comparison with diesel fuel, the blends represented the retarded start of combustion (SOC), the higher peak in-cylinder pressure and heat release rate, and the shortened combustion duration. The brake-specific fuel consumption of the blends was higher by 7% for CD80B20, and 11% for CD60B40, respectively, whereas the difference of the brake thermal efficiency was less than 3%. The blends exhibited bi-modal particle size distribution (PSD), and diesel showed uni-modal behaviour under all operating conditions. The total number concentration of blends was larger, but the geometric mean diameters were 83, 47 and 40 nm for diesel fuel, CD80B20 and CD60B40, respectively. Nitrogen oxide emissions of the blends were higher by 14% and slightly increased with the growth of n-butanol content in the blends. Carbon monoxide (CO) and hydrocarbon (HC) emissions were decreased at 1000 r/min and increased at 1400 r/min

Multi-Sensor Fusion of SDGSAT-1 Thermal Infrared and Multispectral Images

Thermal infrared imagery plays an important role in a variety of fields, such as surface temperature inversion and urban heat island effect analysis, but the spatial resolution has severely restricted the potential for further applications. Data fusion is defined as data combination using multiple sensors, and fused information often has better results than when the sensors are used alone. Since multi-resolution analysis is considered an effective method of image fusion, we propose an MTF-GLP-TAM model to combine thermal infrared (30 m) and multispectral (10 m) information of SDGSAT-1. Firstly, the most relevant multispectral bands to the thermal infrared bands are found. Secondly, to obtain better performance, the high-resolution multispectral bands are histogram-matched with each thermal infrared band. Finally, the spatial details of the multispectral bands are injected into the thermal infrared bands with an MTF Gaussian filter and an additive injection model. Despite the lack of spectral overlap between thermal infrared and multispectral bands, the fused image improves the spatial resolution while maintaining the thermal infrared spectral properties as shown by subjective and objective experimental analyses

Assembly of Protein Cages for Drug Delivery

Nanoparticles (NPs) have been widely used as target delivery vehicles for therapeutic goods; however, compared with inorganic and organic nanomaterials, protein nanomaterials have better biocompatibility and can self-assemble into highly ordered cage-like structures, which are more favorable for applications in targeted drug delivery. In this review, we concentrate on the typical protein cage nanoparticles drugs encapsulation processes, such as drug fusion expression, diffusion, electrostatic contact, covalent binding, and protein cage disassembly/recombination. The usage of protein cage nanoparticles in biomedicine is also briefly discussed. These materials can be utilized to transport small molecules, peptides, siRNA, and other medications for anti-tumor, contrast, etc